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<article id="content">
<header>
<h1 class="title">Module <code>silk.backbones.superpoint.magicpoint</code></h1>
</header>
<section id="section-intro">
<p>The MagicPoint model of SuperPoint to be trained
on synthetic data. Based off of the official
PyTorch implementation from the MagicLeap paper.</p>
<h1 id="checked-parity">Checked Parity</h1>
<h2 id="with-paper-httpsarxivorgpdf171207629pdf">With Paper : <a href="https://arxiv.org/pdf/1712.07629.pdf">https://arxiv.org/pdf/1712.07629.pdf</a></h2>
<h3 id="optimizer-page-6">Optimizer (page 6)</h3>
<ul>
<li>[<strong>done</strong>] Type = Adam</li>
<li>[<strong>done</strong>] Learning Rate = 0.001</li>
<li>[<strong>done</strong>] β = (0.9, 0.999)</li>
</ul>
<h3 id="training-page-6">Training (page 6)</h3>
<ul>
<li>[<strong>done</strong>] Batch Size = 32</li>
<li>[<strong>diff</strong>] Steps = 200,000 (ours : early stopping)</li>
</ul>
<h3 id="metrics-page-4">Metrics (page 4)</h3>
<ul>
<li>[<strong>done</strong>] mAP = 0.971 (ours : 0.999)</li>
</ul>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python"># Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.

# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.

&#34;&#34;&#34;
The MagicPoint model of SuperPoint to be trained
on synthetic data. Based off of the official
PyTorch implementation from the MagicLeap paper.
# Checked Parity
## With Paper : https://arxiv.org/pdf/1712.07629.pdf
### Optimizer (page 6)
* [**done**] Type = Adam
* [**done**] Learning Rate = 0.001
* [**done**] β = (0.9, 0.999)
### Training (page 6)
* [**done**] Batch Size = 32
* [**diff**] Steps = 200,000 (ours : early stopping)
### Metrics (page 4)
* [**done**] mAP = 0.971 (ours : 0.999)
&#34;&#34;&#34;

from functools import partial

import torch
import torch.nn as nn
from silk.backbones.abstract.shared_backbone_multiple_heads import (
    SharedBackboneMultipleHeads,
)
from silk.backbones.silk.coords import (
    mapping_from_torch_module,
    CoordinateMappingProvider,
)
from silk.backbones.superpoint.utils import (
    logits_to_prob,
    depth_to_space,
    prob_map_to_positions_with_prob,
    prob_map_to_points_map,
)
from silk.backbones.superpoint.vgg import vgg_block, VGG
from silk.flow import AutoForward, Flow

Backbone = partial(
    VGG,
    num_channels=1,
    use_batchnorm=True,
    use_max_pooling=True,
)


class DetectorHead(torch.nn.Module, CoordinateMappingProvider):
    def __init__(
        self,
        in_channels: int = 128,
        lat_channels: int = 256,
        out_channels: int = 1,
        use_batchnorm: bool = True,
        padding: int = 1,
        detach: bool = False,
    ) -&gt; None:
        torch.nn.Module.__init__(self)
        CoordinateMappingProvider.__init__(self)

        assert padding in {0, 1}

        self._detach = detach

        self._detH1 = vgg_block(
            in_channels,
            lat_channels,
            3,
            use_batchnorm=use_batchnorm,
            padding=padding,
        )

        if use_batchnorm:
            # no relu (bc last layer) - option to have batchnorm or not
            self._detH2 = nn.Sequential(
                nn.Conv2d(lat_channels, out_channels, 1, padding=0),
                nn.BatchNorm2d(out_channels),
            )
        else:
            # if no batch norm
            self._detH2 = nn.Sequential(
                nn.Conv2d(lat_channels, out_channels, 1, padding=0),
            )

    def mappings(self):
        mapping = mapping_from_torch_module(self._detH1)
        mapping = mapping + mapping_from_torch_module(self._detH2)
        return mapping

    def forward(self, x: torch.Tensor):
        if self._detach:
            x = x.detach()

        x = self._detH1(x)
        x = self._detH2(x)
        return x


class MagicPoint(AutoForward, torch.nn.Module):
    def __init__(
        self,
        *,
        use_batchnorm: bool = True,
        num_channels: int = 1,
        cell_size: int = 8,
        detection_threshold=0.015,
        detection_top_k=None,
        nms_dist=4,
        border_dist=4,
        use_max_pooling: bool = True,
        input_name=&#34;images&#34;,
        backbone=None,
        backbone_output_name: str = &#34;features&#34;,
        detector_head=None,
        detector_head_output_name: str = &#34;logits&#34;,
        default_outputs=None,
    ):
        torch.nn.Module.__init__(self)

        # architecture parameters
        self._num_channels = num_channels
        self._cell_size = cell_size  # depends on VGG&#39;s downsampling

        # detection parameters
        self._detection_threshold = detection_threshold
        self._detection_top_k = detection_top_k
        self._nms_dist = nms_dist
        self._border_dist = border_dist

        # add backbone
        self.backbone = SharedBackboneMultipleHeads(
            backbone=Backbone(
                num_channels=num_channels,
                use_batchnorm=use_batchnorm,
                use_max_pooling=use_max_pooling,
            )
            if backbone is None
            else backbone,
            input_name=input_name,
            backbone_output_name=backbone_output_name,
        )

        if use_max_pooling:
            out_channels = cell_size * cell_size + 1
        else:
            out_channels = 1

        # add detector head
        self.backbone.add_head(
            detector_head_output_name,
            DetectorHead(
                in_channels=128,
                lat_channels=256,
                out_channels=out_channels,
                use_batchnorm=use_batchnorm,
            )
            if detector_head is None
            else detector_head,
        )

        # add the forward function
        default_outputs = (
            (backbone_output_name, detector_head_output_name)
            if default_outputs is None
            else default_outputs
        )
        AutoForward.__init__(self, self.backbone.flow, default_outputs)

        # add detector head post-processing
        MagicPoint.add_detector_head_post_processing(
            self.flow,
            detector_head_output_name=detector_head_output_name,
            prefix=&#34;&#34;,
            cell_size=self._cell_size,
            detection_threshold=self._detection_threshold,
            detection_top_k=self._detection_top_k,
            nms_dist=self._nms_dist,
            border_dist=self._border_dist,
        )

    @staticmethod
    def add_detector_head_post_processing(
        flow: Flow,
        detector_head_output_name: str = &#34;logits&#34;,
        prefix: str = &#34;magicpoint.&#34;,
        cell_size: int = 8,
        detection_threshold=0.015,
        detection_top_k=None,
        nms_dist=4,
        border_dist=4,
    ):
        flow.define_transition(
            f&#34;{prefix}probability&#34;,
            logits_to_prob,
            detector_head_output_name,
        )
        flow.define_transition(
            f&#34;{prefix}score&#34;,
            partial(depth_to_space, cell_size=cell_size),
            f&#34;{prefix}probability&#34;,
        )
        flow.define_transition(
            f&#34;{prefix}nms&#34;,
            partial(
                prob_map_to_points_map,
                prob_thresh=detection_threshold,
                nms_dist=nms_dist,
                border_dist=border_dist,
                top_k=detection_top_k,
            ),
            f&#34;{prefix}score&#34;,
        )
        flow.define_transition(
            f&#34;{prefix}positions&#34;,
            prob_map_to_positions_with_prob,
            f&#34;{prefix}nms&#34;,
        )</code></pre>
</details>
</section>
<section>
</section>
<section>
</section>
<section>
</section>
<section>
<h2 class="section-title" id="header-classes">Classes</h2>
<dl>
<dt id="silk.backbones.superpoint.magicpoint.DetectorHead"><code class="flex name class">
<span>class <span class="ident">DetectorHead</span></span>
<span>(</span><span>in_channels: int = 128, lat_channels: int = 256, out_channels: int = 1, use_batchnorm: bool = True, padding: int = 1, detach: bool = False)</span>
</code></dt>
<dd>
<div class="desc"><p>Base class for all neural network modules.</p>
<p>Your models should also subclass this class.</p>
<p>Modules can also contain other Modules, allowing to nest them in
a tree structure. You can assign the submodules as regular attributes::</p>
<pre><code>import torch.nn as nn
import torch.nn.functional as F

class Model(nn.Module):
    def __init__(self):
        super().__init__()
        self.conv1 = nn.Conv2d(1, 20, 5)
        self.conv2 = nn.Conv2d(20, 20, 5)

    def forward(self, x):
        x = F.relu(self.conv1(x))
        return F.relu(self.conv2(x))
</code></pre>
<p>Submodules assigned in this way will be registered, and will have their
parameters converted too when you call :meth:<code>to</code>, etc.</p>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>As per the example above, an <code>__init__()</code> call to the parent class
must be made before assignment on the child.</p>
</div>
<p>:ivar training: Boolean represents whether this module is in training or
evaluation mode.
:vartype training: bool</p>
<p>Initializes internal Module state, shared by both nn.Module and ScriptModule.</p></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">class DetectorHead(torch.nn.Module, CoordinateMappingProvider):
    def __init__(
        self,
        in_channels: int = 128,
        lat_channels: int = 256,
        out_channels: int = 1,
        use_batchnorm: bool = True,
        padding: int = 1,
        detach: bool = False,
    ) -&gt; None:
        torch.nn.Module.__init__(self)
        CoordinateMappingProvider.__init__(self)

        assert padding in {0, 1}

        self._detach = detach

        self._detH1 = vgg_block(
            in_channels,
            lat_channels,
            3,
            use_batchnorm=use_batchnorm,
            padding=padding,
        )

        if use_batchnorm:
            # no relu (bc last layer) - option to have batchnorm or not
            self._detH2 = nn.Sequential(
                nn.Conv2d(lat_channels, out_channels, 1, padding=0),
                nn.BatchNorm2d(out_channels),
            )
        else:
            # if no batch norm
            self._detH2 = nn.Sequential(
                nn.Conv2d(lat_channels, out_channels, 1, padding=0),
            )

    def mappings(self):
        mapping = mapping_from_torch_module(self._detH1)
        mapping = mapping + mapping_from_torch_module(self._detH2)
        return mapping

    def forward(self, x: torch.Tensor):
        if self._detach:
            x = x.detach()

        x = self._detH1(x)
        x = self._detH2(x)
        return x</code></pre>
</details>
<h3>Ancestors</h3>
<ul class="hlist">
<li>torch.nn.modules.module.Module</li>
<li><a title="silk.backbones.silk.coords.CoordinateMappingProvider" href="../silk/coords.html#silk.backbones.silk.coords.CoordinateMappingProvider">CoordinateMappingProvider</a></li>
</ul>
<h3>Class variables</h3>
<dl>
<dt id="silk.backbones.superpoint.magicpoint.DetectorHead.dump_patches"><code class="name">var <span class="ident">dump_patches</span> : bool</code></dt>
<dd>
<div class="desc"></div>
</dd>
<dt id="silk.backbones.superpoint.magicpoint.DetectorHead.training"><code class="name">var <span class="ident">training</span> : bool</code></dt>
<dd>
<div class="desc"></div>
</dd>
</dl>
<h3>Methods</h3>
<dl>
<dt id="silk.backbones.superpoint.magicpoint.DetectorHead.forward"><code class="name flex">
<span>def <span class="ident">forward</span></span>(<span>self, x: torch.Tensor) ‑> Callable[..., Any]</span>
</code></dt>
<dd>
<div class="desc"><p>Defines the computation performed at every call.</p>
<p>Should be overridden by all subclasses.</p>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>Although the recipe for forward pass needs to be defined within
this function, one should call the :class:<code>Module</code> instance afterwards
instead of this since the former takes care of running the
registered hooks while the latter silently ignores them.</p>
</div></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def forward(self, x: torch.Tensor):
    if self._detach:
        x = x.detach()

    x = self._detH1(x)
    x = self._detH2(x)
    return x</code></pre>
</details>
</dd>
<dt id="silk.backbones.superpoint.magicpoint.DetectorHead.mappings"><code class="name flex">
<span>def <span class="ident">mappings</span></span>(<span>self)</span>
</code></dt>
<dd>
<div class="desc"></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def mappings(self):
    mapping = mapping_from_torch_module(self._detH1)
    mapping = mapping + mapping_from_torch_module(self._detH2)
    return mapping</code></pre>
</details>
</dd>
</dl>
</dd>
<dt id="silk.backbones.superpoint.magicpoint.MagicPoint"><code class="flex name class">
<span>class <span class="ident">MagicPoint</span></span>
<span>(</span><span>*, use_batchnorm: bool = True, num_channels: int = 1, cell_size: int = 8, detection_threshold=0.015, detection_top_k=None, nms_dist=4, border_dist=4, use_max_pooling: bool = True, input_name='images', backbone=None, backbone_output_name: str = 'features', detector_head=None, detector_head_output_name: str = 'logits', default_outputs=None)</span>
</code></dt>
<dd>
<div class="desc"><p>Base class for all neural network modules.</p>
<p>Your models should also subclass this class.</p>
<p>Modules can also contain other Modules, allowing to nest them in
a tree structure. You can assign the submodules as regular attributes::</p>
<pre><code>import torch.nn as nn
import torch.nn.functional as F

class Model(nn.Module):
    def __init__(self):
        super().__init__()
        self.conv1 = nn.Conv2d(1, 20, 5)
        self.conv2 = nn.Conv2d(20, 20, 5)

    def forward(self, x):
        x = F.relu(self.conv1(x))
        return F.relu(self.conv2(x))
</code></pre>
<p>Submodules assigned in this way will be registered, and will have their
parameters converted too when you call :meth:<code>to</code>, etc.</p>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>As per the example above, an <code>__init__()</code> call to the parent class
must be made before assignment on the child.</p>
</div>
<p>:ivar training: Boolean represents whether this module is in training or
evaluation mode.
:vartype training: bool</p>
<p>Initializes internal Module state, shared by both nn.Module and ScriptModule.</p></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">class MagicPoint(AutoForward, torch.nn.Module):
    def __init__(
        self,
        *,
        use_batchnorm: bool = True,
        num_channels: int = 1,
        cell_size: int = 8,
        detection_threshold=0.015,
        detection_top_k=None,
        nms_dist=4,
        border_dist=4,
        use_max_pooling: bool = True,
        input_name=&#34;images&#34;,
        backbone=None,
        backbone_output_name: str = &#34;features&#34;,
        detector_head=None,
        detector_head_output_name: str = &#34;logits&#34;,
        default_outputs=None,
    ):
        torch.nn.Module.__init__(self)

        # architecture parameters
        self._num_channels = num_channels
        self._cell_size = cell_size  # depends on VGG&#39;s downsampling

        # detection parameters
        self._detection_threshold = detection_threshold
        self._detection_top_k = detection_top_k
        self._nms_dist = nms_dist
        self._border_dist = border_dist

        # add backbone
        self.backbone = SharedBackboneMultipleHeads(
            backbone=Backbone(
                num_channels=num_channels,
                use_batchnorm=use_batchnorm,
                use_max_pooling=use_max_pooling,
            )
            if backbone is None
            else backbone,
            input_name=input_name,
            backbone_output_name=backbone_output_name,
        )

        if use_max_pooling:
            out_channels = cell_size * cell_size + 1
        else:
            out_channels = 1

        # add detector head
        self.backbone.add_head(
            detector_head_output_name,
            DetectorHead(
                in_channels=128,
                lat_channels=256,
                out_channels=out_channels,
                use_batchnorm=use_batchnorm,
            )
            if detector_head is None
            else detector_head,
        )

        # add the forward function
        default_outputs = (
            (backbone_output_name, detector_head_output_name)
            if default_outputs is None
            else default_outputs
        )
        AutoForward.__init__(self, self.backbone.flow, default_outputs)

        # add detector head post-processing
        MagicPoint.add_detector_head_post_processing(
            self.flow,
            detector_head_output_name=detector_head_output_name,
            prefix=&#34;&#34;,
            cell_size=self._cell_size,
            detection_threshold=self._detection_threshold,
            detection_top_k=self._detection_top_k,
            nms_dist=self._nms_dist,
            border_dist=self._border_dist,
        )

    @staticmethod
    def add_detector_head_post_processing(
        flow: Flow,
        detector_head_output_name: str = &#34;logits&#34;,
        prefix: str = &#34;magicpoint.&#34;,
        cell_size: int = 8,
        detection_threshold=0.015,
        detection_top_k=None,
        nms_dist=4,
        border_dist=4,
    ):
        flow.define_transition(
            f&#34;{prefix}probability&#34;,
            logits_to_prob,
            detector_head_output_name,
        )
        flow.define_transition(
            f&#34;{prefix}score&#34;,
            partial(depth_to_space, cell_size=cell_size),
            f&#34;{prefix}probability&#34;,
        )
        flow.define_transition(
            f&#34;{prefix}nms&#34;,
            partial(
                prob_map_to_points_map,
                prob_thresh=detection_threshold,
                nms_dist=nms_dist,
                border_dist=border_dist,
                top_k=detection_top_k,
            ),
            f&#34;{prefix}score&#34;,
        )
        flow.define_transition(
            f&#34;{prefix}positions&#34;,
            prob_map_to_positions_with_prob,
            f&#34;{prefix}nms&#34;,
        )</code></pre>
</details>
<h3>Ancestors</h3>
<ul class="hlist">
<li><a title="silk.flow.AutoForward" href="../../flow.html#silk.flow.AutoForward">AutoForward</a></li>
<li>torch.nn.modules.module.Module</li>
</ul>
<h3>Class variables</h3>
<dl>
<dt id="silk.backbones.superpoint.magicpoint.MagicPoint.dump_patches"><code class="name">var <span class="ident">dump_patches</span> : bool</code></dt>
<dd>
<div class="desc"></div>
</dd>
<dt id="silk.backbones.superpoint.magicpoint.MagicPoint.training"><code class="name">var <span class="ident">training</span> : bool</code></dt>
<dd>
<div class="desc"></div>
</dd>
</dl>
<h3>Static methods</h3>
<dl>
<dt id="silk.backbones.superpoint.magicpoint.MagicPoint.add_detector_head_post_processing"><code class="name flex">
<span>def <span class="ident">add_detector_head_post_processing</span></span>(<span>flow: <a title="silk.flow.Flow" href="../../flow.html#silk.flow.Flow">Flow</a>, detector_head_output_name: str = 'logits', prefix: str = 'magicpoint.', cell_size: int = 8, detection_threshold=0.015, detection_top_k=None, nms_dist=4, border_dist=4)</span>
</code></dt>
<dd>
<div class="desc"></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">@staticmethod
def add_detector_head_post_processing(
    flow: Flow,
    detector_head_output_name: str = &#34;logits&#34;,
    prefix: str = &#34;magicpoint.&#34;,
    cell_size: int = 8,
    detection_threshold=0.015,
    detection_top_k=None,
    nms_dist=4,
    border_dist=4,
):
    flow.define_transition(
        f&#34;{prefix}probability&#34;,
        logits_to_prob,
        detector_head_output_name,
    )
    flow.define_transition(
        f&#34;{prefix}score&#34;,
        partial(depth_to_space, cell_size=cell_size),
        f&#34;{prefix}probability&#34;,
    )
    flow.define_transition(
        f&#34;{prefix}nms&#34;,
        partial(
            prob_map_to_points_map,
            prob_thresh=detection_threshold,
            nms_dist=nms_dist,
            border_dist=border_dist,
            top_k=detection_top_k,
        ),
        f&#34;{prefix}score&#34;,
    )
    flow.define_transition(
        f&#34;{prefix}positions&#34;,
        prob_map_to_positions_with_prob,
        f&#34;{prefix}nms&#34;,
    )</code></pre>
</details>
</dd>
</dl>
<h3>Methods</h3>
<dl>
<dt id="silk.backbones.superpoint.magicpoint.MagicPoint.forward"><code class="name flex">
<span>def <span class="ident">forward</span></span>(<span>self, *args, **kwargs) ‑> Callable[..., Any]</span>
</code></dt>
<dd>
<div class="desc"></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def forward(self, *args, **kwargs):
    if self._forward_flow is None:
        self._forward_flow = self._flow.with_outputs(self._default_outputs)
    return self._forward_flow(*args, **kwargs)</code></pre>
</details>
</dd>
</dl>
</dd>
</dl>
</section>
</article>
<nav id="sidebar">
<h1>Index</h1>
<div class="toc">
<ul>
<li><a href="#checked-parity">Checked Parity</a><ul>
<li><a href="#with-paper-httpsarxivorgpdf171207629pdf">With Paper : https://arxiv.org/pdf/1712.07629.pdf</a><ul>
<li><a href="#optimizer-page-6">Optimizer (page 6)</a></li>
<li><a href="#training-page-6">Training (page 6)</a></li>
<li><a href="#metrics-page-4">Metrics (page 4)</a></li>
</ul>
</li>
</ul>
</li>
</ul>
</div>
<ul id="index">
<li><h3>Super-module</h3>
<ul>
<li><code><a title="silk.backbones.superpoint" href="index.html">silk.backbones.superpoint</a></code></li>
</ul>
</li>
<li><h3><a href="#header-classes">Classes</a></h3>
<ul>
<li>
<h4><code><a title="silk.backbones.superpoint.magicpoint.DetectorHead" href="#silk.backbones.superpoint.magicpoint.DetectorHead">DetectorHead</a></code></h4>
<ul class="">
<li><code><a title="silk.backbones.superpoint.magicpoint.DetectorHead.dump_patches" href="#silk.backbones.superpoint.magicpoint.DetectorHead.dump_patches">dump_patches</a></code></li>
<li><code><a title="silk.backbones.superpoint.magicpoint.DetectorHead.forward" href="#silk.backbones.superpoint.magicpoint.DetectorHead.forward">forward</a></code></li>
<li><code><a title="silk.backbones.superpoint.magicpoint.DetectorHead.mappings" href="#silk.backbones.superpoint.magicpoint.DetectorHead.mappings">mappings</a></code></li>
<li><code><a title="silk.backbones.superpoint.magicpoint.DetectorHead.training" href="#silk.backbones.superpoint.magicpoint.DetectorHead.training">training</a></code></li>
</ul>
</li>
<li>
<h4><code><a title="silk.backbones.superpoint.magicpoint.MagicPoint" href="#silk.backbones.superpoint.magicpoint.MagicPoint">MagicPoint</a></code></h4>
<ul class="">
<li><code><a title="silk.backbones.superpoint.magicpoint.MagicPoint.add_detector_head_post_processing" href="#silk.backbones.superpoint.magicpoint.MagicPoint.add_detector_head_post_processing">add_detector_head_post_processing</a></code></li>
<li><code><a title="silk.backbones.superpoint.magicpoint.MagicPoint.dump_patches" href="#silk.backbones.superpoint.magicpoint.MagicPoint.dump_patches">dump_patches</a></code></li>
<li><code><a title="silk.backbones.superpoint.magicpoint.MagicPoint.forward" href="#silk.backbones.superpoint.magicpoint.MagicPoint.forward">forward</a></code></li>
<li><code><a title="silk.backbones.superpoint.magicpoint.MagicPoint.training" href="#silk.backbones.superpoint.magicpoint.MagicPoint.training">training</a></code></li>
</ul>
</li>
</ul>
</li>
</ul>
</nav>
</main>
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